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What are Support Vector Machines (SVM)? - Dataconomy

Support Vector Machines (SVM) are supervised learning algorithms used for classification and regression by finding optimal decision boundaries between data classes.

Support Vector Machines: A Progression of Algorithms

MMC, SVC, SVM: What’s the difference?

SVM and Kernels: The Math that Makes Classification Magic

Imagine you're at a party separating people who love pizza (yum!) from those who...well, have...

A Gentle Introduction to Support Vector Machines

A guide to understanding support vector machines for classification: from theory to scikit-learn implementation.

scikit-and-tensorflow-workbooks/ch05-support-vector-machines.ipynb at master · bjpcjp/scikit-and-tensorflow-workbooks

based on "Hands-On Machine Learning with Scikit-Learn & TensorFlow" (O'Reilly, Aurelien Geron) - bjpcjp/scikit-and-tensorflow-workbooks

SVM Classifier and RBF Kernel — How to Make Better Models in Python

A complete explanation of the inner workings of Support Vector Machines (SVM) and Radial Basis Function (RBF) kernel

SVM Kernels: What Do They Actually Do?

An intuitive visual explanation

Support Vector Machine (SVM) Tutorial: Learning SVMs From Examples

In this post, we will try to gain a high-level understanding of how SVMs work. I’ll focus on developing intuition rather than rigor. What that essentially means is we will skip as much of the math as possible and develop a strong intuition of the working principle.